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What this means

A useful result is more than a successful-looking answer. Check what happened, which sources support it, and whether the intended action completed. A draft, a pending review, a rejected action, and a completed send are different outcomes.

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What to review and prepare

  1. Compare the output with the original request and authoritative records.
  2. Check the actual workflow state before repeating an action.
  3. Record the incorrect answer and a specific correction, with document revision and location.
  4. Have the correction independently reviewed before using it as the basis for a lesson.
  5. Prepare representative evaluation cases with supported reference answers.
Start with Evaluation datasets for case fields and templates, and Build a lesson from an evaluation for the review process.

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How lessons get their evidence

The lesson-building path uses a saved evaluation answer and a reviewed, cited correction. A lesson captures guidance plus when it applies. It must not become a cache of product properties, certification facts, prices, inventory, or lead times. Correct bad business facts in the system of record. Approval is not release. Rule tests, quality gates, release state, and actual workspace activation are separate checks. Do not assume the platform automatically learns from every conversation or applies a candidate across all agents. Consult Lessons learned for the supported lifecycle and limitations.

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How it connects

A failed answer can reveal a problem in the source, the connection, the context, or the workflow’s reasoning. Fix the right layer. More instructions cannot repair an unavailable or stale source record.

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Example

A comparison labels an estimated value as a measured property. Cite the supporting source and correct the answer. A candidate lesson might instruct the supported comparison step to label estimates clearly; it must not store the property value. Test it against examples and counterexamples before considering broader use.

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Limits of evaluation evidence

A passing test is evidence about that test, not proof of production safety or universal improvement. Inspect the dataset, comparison method, scope, and any retrieval limitations. Do not invent time-saved or ROI totals when measured data is unavailable.